A framework for improving localisation prediction algorithms. by Sven B. Gould (12237287)

“…One can expect that the combination of multi-dimensional parameters from evolutionary biology, cell biology and molecular biology on evolutionary diverse species will significantly improve the next generation of machine leaning algorithms that serve localisation (and function) predictions.…”

The actual training effect of four algorithms on original photo data (All images are original images taken by the author). by Xiaojun Wang (50332)

Subjects: “…Cell Biology…”

Folder with all data and algorithms by Jorge Servert Lerdo de Tejada (22290001)

Performance of algorithms outside the training species. by Sven B. Gould (12237287)

City driving speed query results. by Meilin Zhu (688698)

Strong negative correlation between the precision of algorithms and the evolutionary distance from the training data. by Sven B. Gould (12237287)

Targeting prediction algorithms are frequently cited across disciplines and rely on a limited training set. by Sven B. Gould (12237287)

Overview of the Cell2Spatial algorithm. by Huamei Li (8815955)

Subjects: “…Cell Biology…”

Elements: Kingfisher: Storage Management for Data Federations (CSSI PI Meeting 2025) by Brian Bockelman (5128082)

Algorithm testing. by Ziad M. Ali (12516532)

Subjects:

Risk element category diagram. by Yao Hu (3479972)

Supplementary file 1_Earthworm optimization algorithm for extracting parameters for solar cells and photovoltaic modules.docx by Fatima Wardi (22052360)

“…The extracted parameters for each case study are used to reconstruct the I–V and power–voltage (P–V) characteristic curves for the respective solar cell and photovoltaic module technologies. To validate the performance and efficiency of the algorithm, various statistical criteria are computed, including individual absolute error (IAE), relative error (RE), root mean square error (RMSE), mean absolute error (MAE), standard deviation (SD), tracking signal (TS), normalized forecast measure (NFM), and the autocorrelation function (ACF). …”

Convergence curves of the investigated algorithms. by Ziad M. Ali (12516532)

Subjects:

Visual representation of Trust and elements that are needed for equitable data sharing. by Qunita Brown (19751520)

ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

Algorithm pseudocode. by Cheng-jie Chen (22272090)

Subjects: “…Cell Biology…”

Cell-type marker genes and functional enrichment analysis. by Hailin Li (453832)

Subjects:

Completion times for different algorithms. by Jianbin Zheng (587000)

“…In the context of intelligent manufacturing, there is still significant potential for improving the productivity of riveting and welding tasks in existing H-beam riveting and welding work cells. In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. …”

The average cumulative reward of algorithms. by Jianbin Zheng (587000)

“…In the context of intelligent manufacturing, there is still significant potential for improving the productivity of riveting and welding tasks in existing H-beam riveting and welding work cells. In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. …”

Source_data_single_element_motor by Hiroyuki Ichijo (19799292)

“…<p dir="ltr">Source data of experiments by the single-element motor algorithm.…”